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Combining Primary cohort data with external aggregate information without assuming comparability
时间:2021年07月12日 22:18 点击数:

报告人:谌自奇

报告地点:万博ManBetX客户端登录415

报告时间:2021年07月13日星期二09:00-11:00

邀请人:高巍

报告摘要:

In comparative effectiveness research (CER) for rare types of cancer, it is appealing to combine primary cohort data containing detailed tumor profiles together with aggregate information derived from cancer registry databases. Such integration of data may improve statistical efficiency in CER. A major challenge in combining information from different resources, however, is that the aggregate information from the cancer registry databases could be incomparable with the primary cohort data, which are often collected from a single cancer center or a clinical trial. We develop an adaptive estimation procedure, which uses the combined information to determine the degree of information borrowing from the aggregate data of the external resource. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. The proposed method yields a substantial gain in statistical efficiency over the conventional method using the primary cohort only, and avoids undesirable biases when the given external information is incomparable to the primary cohort. We apply the proposed method to evaluate the long-term effect of trimodality treatment to inflammatory breast cancer (IBC) by tumor subtypes, while combining the IBC patient cohort at The University of Texas MD Anderson Cancer Center and the external aggregate information from the National Cancer Data Base.


主讲人简介:

谌自奇,华东师范大学统计学院研究员,博士生导师。研究方向包含高维统计分析、函数型(纵向)数据分析、基于剖面似然的统计推断、生存分析、机器学习等。主持国家自然科学基金面上项目1项、国家自然科学基金青年项目1项、上海市自然科学基金1项、湖南省自然科学基金项目1项、获得中国博士后面上和特别资助。以第一作者或者通讯作者在JASA, Biometric, Statistica Sinica等统计学权威期刊发表(接收)论文10多篇。

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